{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3c5d72f4",
   "metadata": {},
   "source": [
    "# Finetuning a DistilBERT Classifier in Lightning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0873756d-262a-4525-b809-4e0b3a1e63dd",
   "metadata": {},
   "source": [
    "![](figures/finetuning-ii.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6fd9cda8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "92ea5612",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fe7191cf-62ed-4793-8358-bee70b233d05",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install lightning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cfd724d",
   "metadata": {},
   "source": [
    "# 1 Loading the Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd06d930",
   "metadata": {},
   "source": [
    "The IMDB movie review dataset consists of 50k movie reviews with sentiment label (0: negative, 1: positive)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60fe0b76",
   "metadata": {},
   "source": [
    "## 1a) Load from `datasets` Hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "447e24bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import list_datasets, load_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2baf2f16",
   "metadata": {},
   "outputs": [],
   "source": [
    "# list_datasets()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6310d5bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset imdb (/home/sebastian/.cache/huggingface/datasets/imdb/plain_text/1.0.0/d613c88cf8fa3bab83b4ded3713f1f74830d1100e171db75bbddb80b3345c9c0)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1b7cb09fb60242bc802cd825c40aec72",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatasetDict({\n",
      "    train: Dataset({\n",
      "        features: ['text', 'label'],\n",
      "        num_rows: 25000\n",
      "    })\n",
      "    test: Dataset({\n",
      "        features: ['text', 'label'],\n",
      "        num_rows: 25000\n",
      "    })\n",
      "    unsupervised: Dataset({\n",
      "        features: ['text', 'label'],\n",
      "        num_rows: 50000\n",
      "    })\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "imdb_data = load_dataset(\"imdb\")\n",
    "print(imdb_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "552bbb2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'text': \"This film is terrible. You don't really need to read this review further. If you are planning on watching it, suffice to say - don't (unless you are studying how not to make a good movie).<br /><br />The acting is horrendous... serious amateur hour. Throughout the movie I thought that it was interesting that they found someone who speaks and looks like Michael Madsen, only to find out that it is actually him! A new low even for him!!<br /><br />The plot is terrible. People who claim that it is original or good have probably never seen a decent movie before. Even by the standard of Hollywood action flicks, this is a terrible movie.<br /><br />Don't watch it!!! Go for a jog instead - at least you won't feel like killing yourself.\",\n",
       " 'label': 0}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb_data[\"train\"][99]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40bdb9c5",
   "metadata": {},
   "source": [
    "## 1b) Load from local directory"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9103ec2d",
   "metadata": {},
   "source": [
    "The IMDB movie review set can be downloaded from http://ai.stanford.edu/~amaas/data/sentiment/. After downloading the dataset, decompress the files.\n",
    "\n",
    "A) If you are working with Linux or MacOS X, open a new terminal window cd into the download directory and execute\n",
    "\n",
    "    tar -zxf aclImdb_v1.tar.gz\n",
    "\n",
    "B) If you are working with Windows, download an archiver such as 7Zip to extract the files from the download archive."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac508bb8",
   "metadata": {},
   "source": [
    "C) Use the following code to download and unzip the dataset via Python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "241ecc96",
   "metadata": {},
   "source": [
    "**Download the movie reviews**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "02aeade4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100% | 80.23 MB | 2.64 MB/s | 30.34 sec elapsed"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import tarfile\n",
    "import time\n",
    "import urllib.request\n",
    "\n",
    "source = \"http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\"\n",
    "target = \"aclImdb_v1.tar.gz\"\n",
    "\n",
    "if os.path.exists(target):\n",
    "    os.remove(target)\n",
    "\n",
    "\n",
    "def reporthook(count, block_size, total_size):\n",
    "    global start_time\n",
    "    if count == 0:\n",
    "        start_time = time.time()\n",
    "        return\n",
    "    duration = time.time() - start_time\n",
    "    progress_size = int(count * block_size)\n",
    "    speed = progress_size / (1024.0**2 * duration)\n",
    "    percent = count * block_size * 100.0 / total_size\n",
    "\n",
    "    sys.stdout.write(\n",
    "        f\"\\r{int(percent)}% | {progress_size / (1024.**2):.2f} MB \"\n",
    "        f\"| {speed:.2f} MB/s | {duration:.2f} sec elapsed\"\n",
    "    )\n",
    "    sys.stdout.flush()\n",
    "\n",
    "\n",
    "if not os.path.isdir(\"aclImdb\") and not os.path.isfile(\"aclImdb_v1.tar.gz\"):\n",
    "    urllib.request.urlretrieve(source, target, reporthook)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2a867dcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isdir(\"aclImdb\"):\n",
    "\n",
    "    with tarfile.open(target, \"r:gz\") as tar:\n",
    "        tar.extractall()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9318d4d0",
   "metadata": {},
   "source": [
    "**Convert them to a pandas DataFrame and save them as CSV**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "464e587c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████| 50000/50000 [00:27<00:00, 1809.68it/s]\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from packaging import version\n",
    "from tqdm import tqdm\n",
    "\n",
    "# change the `basepath` to the directory of the\n",
    "# unzipped movie dataset\n",
    "\n",
    "basepath = \"aclImdb\"\n",
    "\n",
    "labels = {\"pos\": 1, \"neg\": 0}\n",
    "\n",
    "df = pd.DataFrame()\n",
    "\n",
    "with tqdm(total=50000) as pbar:\n",
    "    for s in (\"test\", \"train\"):\n",
    "        for l in (\"pos\", \"neg\"):\n",
    "            path = os.path.join(basepath, s, l)\n",
    "            for file in sorted(os.listdir(path)):\n",
    "                with open(os.path.join(path, file), \"r\", encoding=\"utf-8\") as infile:\n",
    "                    txt = infile.read()\n",
    "\n",
    "                if version.parse(pd.__version__) >= version.parse(\"1.3.2\"):\n",
    "                    x = pd.DataFrame(\n",
    "                        [[txt, labels[l]]], columns=[\"review\", \"sentiment\"]\n",
    "                    )\n",
    "                    df = pd.concat([df, x], ignore_index=False)\n",
    "\n",
    "                else:\n",
    "                    df = df.append([[txt, labels[l]]], ignore_index=True)\n",
    "                pbar.update()\n",
    "df.columns = [\"text\", \"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "02649593",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.random.seed(0)\n",
    "df = df.reindex(np.random.permutation(df.index))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59ca0386",
   "metadata": {},
   "source": [
    "**Basic datasets analysis and sanity checks**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c2db547a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class distribution:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([25000, 25000])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Class distribution:\")\n",
    "np.bincount(df[\"label\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a007e612",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 173.0, 2470)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_len = df[\"text\"].apply(lambda x: len(x.split()))\n",
    "text_len.min(), text_len.median(), text_len.max() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00f4b04d",
   "metadata": {},
   "source": [
    "**Split data into training, validation, and test sets**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ff703901",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_shuffled = df.sample(frac=1, random_state=1).reset_index()\n",
    "\n",
    "df_train = df_shuffled.iloc[:35_000]\n",
    "df_val = df_shuffled.iloc[35_000:40_000]\n",
    "df_test = df_shuffled.iloc[40_000:]\n",
    "\n",
    "df_train.to_csv(\"train.csv\", index=False, encoding=\"utf-8\")\n",
    "df_val.to_csv(\"validation.csv\", index=False, encoding=\"utf-8\")\n",
    "df_test.to_csv(\"test.csv\", index=False, encoding=\"utf-8\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "846d83b1",
   "metadata": {},
   "source": [
    "# 2 Tokenization and Numericalization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bd5f770",
   "metadata": {},
   "source": [
    "**Load the dataset via `load_dataset`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a1aa66c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration default-2352009af2284f6d\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset csv/default to /home/sebastian/.cache/huggingface/datasets/csv/default-2352009af2284f6d/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "acea2a6e77cd4cde9e9fd5d112a35f69",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c1c067de4083435fafd7627871abc92c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sebastian/miniforge3/envs/lightning2/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\n",
      "  return pd.read_csv(xopen(filepath_or_buffer, \"rb\", use_auth_token=use_auth_token), **kwargs)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating validation split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sebastian/miniforge3/envs/lightning2/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\n",
      "  return pd.read_csv(xopen(filepath_or_buffer, \"rb\", use_auth_token=use_auth_token), **kwargs)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating test split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset csv downloaded and prepared to /home/sebastian/.cache/huggingface/datasets/csv/default-2352009af2284f6d/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sebastian/miniforge3/envs/lightning2/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\n",
      "  return pd.read_csv(xopen(filepath_or_buffer, \"rb\", use_auth_token=use_auth_token), **kwargs)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a6bf5b53e94c47e4b40c8647b78fb44c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatasetDict({\n",
      "    train: Dataset({\n",
      "        features: ['index', 'text', 'label'],\n",
      "        num_rows: 35000\n",
      "    })\n",
      "    validation: Dataset({\n",
      "        features: ['index', 'text', 'label'],\n",
      "        num_rows: 5000\n",
      "    })\n",
      "    test: Dataset({\n",
      "        features: ['index', 'text', 'label'],\n",
      "        num_rows: 10000\n",
      "    })\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "imdb_dataset = load_dataset(\n",
    "    \"csv\",\n",
    "    data_files={\n",
    "        \"train\": \"train.csv\",\n",
    "        \"validation\": \"validation.csv\",\n",
    "        \"test\": \"test.csv\",\n",
    "    },\n",
    ")\n",
    "\n",
    "print(imdb_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "029adc8f-cdfe-4386-9552-a1120f49adee",
   "metadata": {},
   "source": [
    "**Tokenize the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5ea762ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tokenizer input max length: 512\n",
      "Tokenizer vocabulary size: 30522\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
    "print(\"Tokenizer input max length:\", tokenizer.model_max_length)\n",
    "print(\"Tokenizer vocabulary size:\", tokenizer.vocab_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8432c15c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize_text(batch):\n",
    "    return tokenizer(batch[\"text\"], truncation=True, padding=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0bb392cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ed0b8624e0ea4400ab0967bba8998e4b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c8d4c4c04a6b45bea53800338047c761",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "94972c84d1504fab964c388114017010",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imdb_tokenized = imdb_dataset.map(tokenize_text, batched=True, batch_size=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6d4103c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "del imdb_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "89ef894c-978f-47f2-9d61-cb6a9f38e745",
   "metadata": {},
   "outputs": [],
   "source": [
    "imdb_tokenized.set_format(\"torch\", columns=[\"input_ids\", \"attention_mask\", \"label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0ea67091-aeb7-46c1-871f-638ce58d8a0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c4f8cd8-e641-45fb-9893-70677631917a",
   "metadata": {},
   "source": [
    "# 3 Set Up DataLoaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0807b068-7d8f-4055-a26a-177e07dea4c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader, Dataset\n",
    "\n",
    "\n",
    "class IMDBDataset(Dataset):\n",
    "    def __init__(self, dataset_dict, partition_key=\"train\"):\n",
    "        self.partition = dataset_dict[partition_key]\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return self.partition[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.partition.num_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "90cb08f3-ef77-4351-8b19-42d99dd24f98",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = IMDBDataset(imdb_tokenized, partition_key=\"train\")\n",
    "val_dataset = IMDBDataset(imdb_tokenized, partition_key=\"validation\")\n",
    "test_dataset = IMDBDataset(imdb_tokenized, partition_key=\"test\")\n",
    "\n",
    "train_loader = DataLoader(\n",
    "    dataset=train_dataset,\n",
    "    batch_size=64,\n",
    "    shuffle=True, \n",
    "    num_workers=4\n",
    ")\n",
    "\n",
    "val_loader = DataLoader(\n",
    "    dataset=val_dataset,\n",
    "    batch_size=64,\n",
    "    num_workers=4\n",
    ")\n",
    "\n",
    "test_loader = DataLoader(\n",
    "    dataset=test_dataset,\n",
    "    batch_size=64,\n",
    "    num_workers=4\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78e774ab-45a0-4c48-ad61-a3d0e1927ef4",
   "metadata": {},
   "source": [
    "# 4 Initializing DistilBERT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dc28ddbe-1a96-4c24-9f5c-40ffdca4a572",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_layer_norm.weight', 'vocab_projector.weight', 'vocab_transform.bias', 'vocab_transform.weight']\n",
      "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'pre_classifier.weight', 'classifier.weight', 'pre_classifier.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModelForSequenceClassification\n",
    "\n",
    "model = AutoModelForSequenceClassification.from_pretrained(\n",
    "    \"distilbert-base-uncased\", num_labels=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "def1cf25-0a7d-4bb2-9419-b7a8fe1c1eab",
   "metadata": {},
   "source": [
    "## 5 Finetuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "534f7a59-2c86-4895-ad7c-2cdd675b003a",
   "metadata": {},
   "source": [
    "**Wrap in LightningModule for Training**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9f2c474d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightning as L\n",
    "import torch\n",
    "import torchmetrics\n",
    "\n",
    "\n",
    "class LightningModel(L.LightningModule):\n",
    "    def __init__(self, model, learning_rate=5e-5):\n",
    "        super().__init__()\n",
    "\n",
    "        self.learning_rate = learning_rate\n",
    "        self.model = model\n",
    "\n",
    "        self.val_acc = torchmetrics.Accuracy(task=\"multiclass\", num_classes=2)\n",
    "        self.test_acc = torchmetrics.Accuracy(task=\"multiclass\", num_classes=2)\n",
    "\n",
    "    def forward(self, input_ids, attention_mask, labels):\n",
    "        return self.model(input_ids, attention_mask=attention_mask, labels=labels)\n",
    "        \n",
    "    def training_step(self, batch, batch_idx):\n",
    "        outputs = self(batch[\"input_ids\"], attention_mask=batch[\"attention_mask\"],\n",
    "                       labels=batch[\"label\"])        \n",
    "        self.log(\"train_loss\", outputs[\"loss\"])\n",
    "        return outputs[\"loss\"]  # this is passed to the optimizer for training\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        outputs = self(batch[\"input_ids\"], attention_mask=batch[\"attention_mask\"],\n",
    "                       labels=batch[\"label\"])        \n",
    "        self.log(\"val_loss\", outputs[\"loss\"], prog_bar=True)\n",
    "        \n",
    "        logits = outputs[\"logits\"]\n",
    "        predicted_labels = torch.argmax(logits, 1)\n",
    "        self.val_acc(predicted_labels, batch[\"label\"])\n",
    "        self.log(\"val_acc\", self.val_acc, prog_bar=True)\n",
    "        \n",
    "    def test_step(self, batch, batch_idx):\n",
    "        outputs = self(batch[\"input_ids\"], attention_mask=batch[\"attention_mask\"],\n",
    "                       labels=batch[\"label\"])        \n",
    "        \n",
    "        logits = outputs[\"logits\"]\n",
    "        predicted_labels = torch.argmax(logits, 1)\n",
    "        self.test_acc(predicted_labels, batch[\"label\"])\n",
    "        self.log(\"accuracy\", self.test_acc, prog_bar=True)\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n",
    "        return optimizer\n",
    "    \n",
    "\n",
    "lightning_model = LightningModel(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e6dab813-e1fc-47cd-87a1-5eb8070699c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from lightning.pytorch.callbacks import ModelCheckpoint\n",
    "from lightning.pytorch.loggers import CSVLogger\n",
    "\n",
    "\n",
    "callbacks = [\n",
    "    ModelCheckpoint(\n",
    "        save_top_k=1, mode=\"max\", monitor=\"val_acc\"\n",
    "    )  # save top 1 model\n",
    "]\n",
    "logger = CSVLogger(save_dir=\"logs/\", name=\"my-model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "492aa043-02da-459e-a266-091b34254ac6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using bfloat16 Automatic Mixed Precision (AMP)\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
      "Missing logger folder: logs/my-model\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n",
      "\n",
      "  | Name     | Type                                | Params\n",
      "-----------------------------------------------------------------\n",
      "0 | model    | DistilBertForSequenceClassification | 67.0 M\n",
      "1 | val_acc  | MulticlassAccuracy                  | 0     \n",
      "2 | test_acc | MulticlassAccuracy                  | 0     \n",
      "-----------------------------------------------------------------\n",
      "67.0 M    Trainable params\n",
      "0         Non-trainable params\n",
      "67.0 M    Total params\n",
      "133.910   Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "26fa7b1ba00849a597fdd5e03b7872bf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=3` reached.\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "trainer = L.Trainer(\n",
    "    max_epochs=3,\n",
    "    callbacks=callbacks,\n",
    "    precision=\"bf16\",\n",
    "    accelerator=\"gpu\",\n",
    "    devices=[1],\n",
    "    logger=logger,\n",
    "    log_every_n_steps=10,\n",
    "    deterministic=True\n",
    ")\n",
    "\n",
    "start = time.time()\n",
    "trainer.fit(model=lightning_model,\n",
    "            train_dataloaders=train_loader,\n",
    "            val_dataloaders=val_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b6116cbc-f2c5-43c0-bb76-737be4164752",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed 6.12 min\n"
     ]
    }
   ],
   "source": [
    "end = time.time()\n",
    "elapsed = end-start\n",
    "print(f\"Time elapsed {elapsed/60:.2f} min\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "eeb92de4-d483-4627-b9f3-f0bba0cddd9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
      "Restoring states from the checkpoint path at logs/my-model/version_0/checkpoints/epoch=2-step=1641.ckpt\n",
      "Lightning automatically upgraded your loaded checkpoint from v2.0.0dev to v2.0.0dev. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint --file logs/my-model/version_0/checkpoints/epoch=2-step=1641.ckpt`\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n",
      "Loaded model weights from the checkpoint at logs/my-model/version_0/checkpoints/epoch=2-step=1641.ckpt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cbeb91cb6ee34932bb268f9fab708fdd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">         accuracy          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.9277999997138977     </span>│\n",
       "└───────────────────────────┴───────────────────────────┘\n",
       "</pre>\n"
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      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[36m \u001b[0m\u001b[36m        accuracy         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.9277999997138977    \u001b[0m\u001b[35m \u001b[0m│\n",
       "└───────────────────────────┴───────────────────────────┘\n"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "[{'accuracy': 0.9277999997138977}]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.test(lightning_model, dataloaders=test_loader, ckpt_path=\"best\")"
   ]
  }
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